{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mmgcn-multimodal-fusion-via-deep-graph","title":"MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation","arxiv_id":"2107.06779","date":"2021-07-14","proceeding":"ACL 2021 5","authors":["Jingwen Hu","Yuchen Liu","Jinming Zhao","Qin Jin"],"abstract":"Emotion recognition in conversation (ERC) is a crucial component in affective dialogue systems, which helps the system understand users' emotions and generate empathetic responses. However, most works focus on modeling speaker and contextual information primarily on the textual modality or simply leveraging multimodal information through feature concatenation. In order to explore a more effective way of utilizing both multimodal and long-distance contextual information, we propose a new model based on multimodal fused graph convolutional network, MMGCN, in this work. MMGCN can not only make use of multimodal dependencies effectively, but also leverage speaker information to model inter-speaker and intra-speaker dependency. We evaluate our proposed model on two public benchmark datasets, IEMOCAP and MELD, and the results prove the effectiveness of MMGCN, which outperforms other SOTA methods by a significant margin under the multimodal conversation setting.","url_abs":"https://arxiv.org/abs/2107.06779v1","url_pdf":"https://arxiv.org/pdf/2107.06779v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mmgcn-multimodal-fusion-via-deep-graph","repo_url":"https://github.com/hujingwen6666/MMGCN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"emotion-recognition-in-conversation","task_name":"Emotion Recognition in Conversation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-cmu-2","task":"Emotion Recognition in Conversation","dataset":"CMU-MOSEI-Sentiment","model":"MMGCN","rank_in_archive_order":2,"of":7,"metrics":{"Accuracy":"45.67","Weighted F1":"44.11"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-7","task":"Emotion Recognition in Conversation","dataset":"IEMOCAP-4","model":"MMGCN","rank_in_archive_order":8,"of":8,"metrics":{"Accuracy":"79.75","Weighted F1":"79.72"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2107.06779","atlas_url":"https://app.syntology.ai/?focus=2107.06779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.06779"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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